Integrated Microarray to Identify the Hub miRNAs and Constructed miRNA-mRNA Network in Neuroblastoma Via Bioinformatics Analysis

Whole genome resequencing-based analysis of plateau adaptation in Meiren yak (Bos grunniens)

The Meiren yak is an important genetic resource in Gansu Province, China. In this study, we aimed to explore the evolutionary history and population structure of the genetic resource of Meiren yak and to mine the characteristic genes of Meiren yak. We analysed a total of 93 yaks of eight yak breeds based on whole genome resequencing combined with population genomics and used θπ ratio and Fst method to screen the selected sites in the genome region. The results proved that Meiren yak can be used as a potential genetic resource in Gansu Province. The genes in Meiren yak with positive selection in selection signal analysis were subjected to the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, which indicated that the genes were related to the adaptability to high altitude and hypoxic environment. By analysing the genetic variation of Meiren yak at the genome-wide level, this study provided a theoretical basis for genetic improvement of Meiren yak and for the development of high-quality yak resources.

Comprehensive analysis and validation reveal potential MYCN regulatory biomarkers associated with neuroblastoma prognosis

Neuroblastoma (NB) is an embryonic malignant tumor that occurs in the sympathetic nervous system. The treatment results of patients in the high-risk group are poor, and relapse and treatment failure can occur even with multiple combination treatments. The proto-oncogene MYCN is a BHLH Transcription Factor used as an independent prognostic factor for NB. The proportion of MYCN amplification in tumor tissues of high-risk patients reaches 40-50%. Hence, exploring new MYCN target genes is a meaningful approach in developing treatment for high-risk NB patients. The microarray datasets were obtained from Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and miRPathDB were used for enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and for modular analysis. The miRNet and NetworkAnalyst databases were used to predict and construct gene-miRNA and gene-TFs networks. The R2 database was used for expression, correlation, and prognostic analyses. The diagnostic value of the biomarkers was predicted by ROC analysis, and RT-qPCR was used to validate the identified hub genes. Finally, using specific MYCN siRNA and overexpressing plasmids, the correlation between the identified hub genes and MYCN was investigated. Our results showed that FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are novel biomarkers that affect the prognosis of the NB patients. In addition, FBXO9, RNF19B, and TRIM36 were preliminarily confirmed as potential target genes of MYCN. Overall, FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are expected to become novel biomarkers for the treatment of high-risk NB patients.Communicated by Ramaswamy H. Sarma.

Systems biology based miRNA-mRNA expression pattern analysis of Emodin in breast cancer cell lines

Breast cancer has been among the most prominent cancers with high mortality. Currently most of the offered therapeutics are toxic; hence, less toxic therapeutic intervention is required. Here, we studied the molecular mechanisms of the effect of a phytoestrogen Emodin on estrogen receptor positive MCF-7 and negative MDA-MB-231 cells by carrying out a comprehensive network assessment. Differentially expressed microRNAs along with their previously identified differentially expressed mRNAs were analyzed through microarrays by using integrative systems biology approach. For each cell line miRNA-target gene networks were built, gene ontology and pathway enrichment analyses were performed, enrichment maps were constructed and the potential key genes, miRNAs and miRNA-gene interactions were studied.

Myocardial infarction unveiled: Key miRNA players screened by a novel lncRNA-miRNA-mRNA network model

BACKGROUND

Myocardial infarction (MI) is a major contributor to global mortality, and microRNAs (miRNAs) are important in its pathogenesis. Identifying blood miRNAs with clinical application potential for the early detection and treatment of MI is crucial.

METHODS

We obtained MI-related miRNA and miRNA microarray datasets from MI Knowledge Base (MIKB) and Gene Expression Omnibus (GEO), respectively. A new feature called target regulatory score (TRS) was proposed to characterize the RNA interaction network. MI-related miRNAs were characterized using TRS, transcription factor (TF) gene proportion (TFP), and ageing-related gene (AG) proportion (AGP) via the lncRNA-miRNA-mRNA network. A bioinformatics model was then developed to predict MI-related miRNAs, which were verified by literature and pathway enrichment analysis.

RESULTS

The TRS-characterized model outperformed previous methods in identifying MI-related miRNAs. MI-related miRNAs had high TRS, TFP, and AGP values, and combining the three features improved prediction accuracy to 0.743. With this method, 31 candidate MI-related miRNAs were screened from the specific-MI lncRNA-miRNA-mRNA network, associated with key MI pathways like circulatory system processes, inflammatory response, and oxygen level adaptation. Most candidate miRNAs were directly associated with MI according to literature evidence, except hsa-miR-520c-3p and hsa-miR-190b-5p. Furthermore, CAV1, PPARA and VEGFA were identified as MI key genes, and were targeted by most of the candidate miRNAs.

CONCLUSIONS

This study proposed a novel bioinformatics model based on multivariate biomolecular network analysis to identify putative key miRNAs of MI, which deserve further experimental and clinical validation for translational applications.

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Antioxidant Effects and Potential Molecular Mechanism of Action of Limonium aureum Extract Based on Systematic Network Pharmacology

Oxidative stress is the redox imbalance state of organisms that involves in a variety of biological processes of diseases. Limonium aureum (L.) Hill. is an excellent wild plant resource in northern China, which has potential application value for treating oxidative stress. However, there are few studies that focused on the antioxidant effect and related mechanism of L. aureum. Thus, the present study combining systematic network pharmacology and molecular biology aimed to investigate the antioxidant effects of L. aureum and explore its underlying anti-oxidation mechanisms. First, the antioxidant activity of L. aureum extracts was confirmed by in vitro and intracellular antioxidant assays. Then, a total of 11 bioactive compounds, 102 predicted targets, and 70 antioxidant-related targets were obtained from open source databases. For elucidating the molecular mechanisms of L. aureum, the PPI network and integrated visualization network based on bioinformatics assays were constructed to preliminarily understand the active compounds and related targets. The subsequent enrichment analysis results showed that L. aureum mainly affect the biological processes involving oxidation-reduction process, response to drug, etc., and the interference with these biological processes might be due to the simultaneous influence on multiple signaling pathways, including the HIF-1 and ERBB signaling pathways. Moreover, the mRNA levels of predicted hub genes were measured by qRT-PCR to verify the regulatory effect of L. aureum on them. Collectively, this finding lays a foundation for further elucidating the anti-oxidative damage mechanism of L. aureum and promotes the development of therapeutic drugs for oxidative stress.

A regulatory miRNA-mRNA network is associated with transplantation response in acute kidney injury

Background

Acute kidney injury (AKI) is a life-threatening complication characterized by rapid decline in renal function, which frequently occurs after transplantation surgery. However, the molecular mechanism underlying the development of post-transplant (post-Tx) AKI still remains unknown. An increasing number of studies have demonstrated that certain microRNAs (miRNAs) exert crucial functions in AKI. The present study sought to elucidate the molecular mechanisms in post-Tx AKI by constructing a regulatory miRNA–mRNA network.

Results

Based on two datasets (GSE53771 and GSE53769), three key modules, which contained 55 mRNAs, 76 mRNAs, and 151 miRNAs, were identified by performing weighted gene co-expression network analysis (WGCNA). The miRDIP v4.1 was applied to predict the interactions of key module mRNAs and miRNAs, and the miRNA–mRNA pairs with confidence of more than 0.2 were selected to construct a regulatory miRNA–mRNA network by Cytoscape. The miRNA–mRNA network consisted of 82 nodes (48 mRNAs and 34 miRNAs) and 125 edges. Two miRNAs (miR-203a-3p and miR-205-5p) and ERBB4 with higher node degrees compared with other nodes might play a central role in post-Tx AKI. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that this network was mainly involved in kidney-/renal-related functions and PI3K–Akt/HIF-1/Ras/MAPK signaling pathways.

Conclusion

We constructed a regulatory miRNA–mRNA network to provide novel insights into post-Tx AKI development, which might help discover new biomarkers or therapeutic drugs for enhancing the ability for early prediction and intervention and decreasing mortality rate of AKI after transplantation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00363-y.

Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection

BACKGROUND

Kidney transplantation is an optimal method for treatment of end-stage kidney failure. However, kidney transplant rejection (KTR) is commonly observed to have negative effects on allograft function. MicroRNAs (miRNAs) are small non-coding RNAs with regulatory role in KTR genesis, the identification of miRNA biomarkers for accurate diagnosis and subtyping of KTR is therefore of clinical significance for active intervention and personalized therapy.

METHODS

In this study, an integrative bioinformatics model was developed based on multi-omics network characterization for miRNA biomarker discovery in KTR. Compared with existed methods, the topological importance of miRNA targets was prioritized based on cross-level miRNA-mRNA and protein-protein interaction network analyses. The biomarker potential of identified miRNAs was computationally validated and explored by receiver-operating characteristic (ROC) evaluation and integrated "miRNA-gene-pathway" pathogenic survey.

RESULTS

Three miRNAs, i.e., miR-145-5p, miR-155-5p, and miR-23b-3p, were screened as putative biomarkers for KTR monitoring. Among them, miR-155-5p was a previously reported signature in KTR, whereas the remaining two were novel candidates both for KTR diagnosis and subtyping. The ROC analysis convinced the power of identified miRNAs as single and combined biomarkers for KTR prediction in kidney tissue and blood samples. Functional analyses, including the latent crosstalk among HLA-related genes, immune signaling pathways and identified miRNAs, provided new insights of these miRNAs in KTR pathogenesis.

CONCLUSIONS

A network-based bioinformatics approach was proposed and applied to identify candidate miRNA biomarkers for KTR study. Biological and clinical validations are further needed for translational applications of the findings.

Long Noncoding RNA LINC01410 Suppresses Tumorigenesis and Enhances Radiosensitivity in Neuroblastoma Cells Through Regulating miR-545-3p/HK2 Axis

Background

Abnormal expression of long noncoding RNAs (lncRNAs) was often involved in tumorigenesis and radiosensitivity of various cancers. The aim of this study was to explore the biological function and regulatory mechanism of lncRNA long intergenic non-protein coding RNA 1410 (LINC01410) in tumorigenesis and radiosensitivity of neuroblastoma (NB).

Methods

The expression of LINC01410, microRNA-329-3p (miR-545-3p) and hexokinase 2 (HK2) was detected by quantitative real-time polymerase chain reaction (qRT-PCR). Methylthiazolyldiphenyl tetrazolium bromide (MTT) assay, colony formation assay and transwell assay were utilized to detect cell viability, colony formation and cell invasion abilities. Glucose consumption or lactate production was measured by glucose assay kit or lactate assay kit, respectively. The interaction between miR-545-3p and LINC01410 or HK2 was predicted by starBase v2.0 and verified by dual-luciferase reporter, RNA Immunoprecipitation (RIP) and RNA pull-down assays. Western blot was used to measure the protein expression of HK2. The mice xenograft model was established to investigate the role of LINC01410 in vivo.

Results

LINC01410 and HK2 were highly expressed while miR-545-3p was lowly expressed in NB tissues and cells. LINC01410 knockdown inhibited tumorigenesis by repressing cell proliferation and invasion, and increased the radiosensitivity via inhibiting colony formation rates and glycolysis. LINC01410 knockdown also suppressed tumor growth in vivo. Moreover, miR-545-3p could bind to LINC01410 and its downregulation reversed the effects of LINC01410 knockdown on tumorigenesis and radiosensitivity. Additionally, HK2 was a direct target of miR-545-3p and its overexpression attenuated the effects of miR-545-3p restoration on suppression of tumorigenesis and promotion of radiosensitivity. Besides, LINC01410 functioned as a molecular sponge of miR-545-3p to regulate HK2 expression.

Conclusion

LINC01410 interference inhibited tumorigenesis and increased radiosensitivity via regulating miR-545-3p/HK2 axis, providing a novel therapeutic strategy for NB.

Baicalin-Aluminum Modulates the Broiler Gut Microbiome

Baicalin-aluminum regulates the gut microbiome of piglets with diarrhea. However, whether it affects poultry gut microbiome composition and function remains unknown. In this study, we used metagenomic sequencing to explore the effects of baicalin-aluminum on gut microbiome changes in poultry when compared with animals administered colistin sulfate. Our data showed that important gut microbiome components consisted of Ruminococcaceae, Subdoligranulum, Bifidobacterium, Bifidobacterium pseudolongum, and Pseudoflavonifractor when broilers were administered baicalin-aluminum compared with colistin. At the species level, Lactobacillus salivarius, Bacteroides uniformis, Oscillibacter unclassified, Bacteroides fragilis, Ruminococcus torques, and Subdoligranulum unclassified abundance were significantly upregulated upon baicalin-aluminum treatment when compared with colistin administration. In addition, Gene Ontology (GO) enrichment analysis indicated that functional differentially expressed genes, which were in the top 30 GO enrichment terms, were associated with metabolic processes, catalytic activity, and cellular processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that ABC transporters, oxidative phosphorylation, and phosphotransferase systems were the dominant signaling pathways in the baicalin-aluminum group when compared with the colistin group. Taken together, our data indicated that baicalin-aluminum modified broiler gut microbiome composition. These observations enhance our physiological insights of baicalin-aluminum-mediated functions in the broiler microbiome and potentially provide a novel therapy to manage both animal and human health.

Development of cancer prognostic signature based on pan-cancer proteomics

Utilizing genomic data to predict cancer prognosis was insufficient. Proteomics can improve our understanding of the etiology and progression of cancer and improve the assessment of cancer prognosis. And the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has generated extensive proteomics data of the vast majority of tumors. Based on CPTAC, we can perform a proteomic pan-carcinoma analysis. We collected the proteomics data and clinical features of cancer patients from CPTAC. Then, we screened 69 differentially expressed proteins (DEPs) with R software in five cancers: hepatocellular carcinoma (HCC), children’s brain tumor tissue consortium (CBTTC), clear cell renal cell carcinoma (CCRC), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC). GO and KEGG analysis were performed to clarify the function of these proteins. We also identified their interactions. The DEPs-based prognostic model for predicting over survival was identified by least absolute shrinkage and selection operator (LASSO)-Cox regression model in training cohort. Then, we used the time-dependent receiver operating characteristics analysis to evaluate the ability of the prognostic model to predict overall survival and validated it in validation cohort. The results showed that the DEPs-based prognostic model could accurately and effectively predict the survival rate of most cancers.